Understanding data analytics vs AI

Evan Smith's article explores the evolving relationship between data analytics and artificial intelligence (AI) and how data architecture can support both fields. While analytics focuses on accessing, transforming, and querying data to derive business insights, AI offers a complementary approach by using probabilistic models for predictions and generating outputs. Despite their differences, both require robust data architectures, with analytics relying on structured data and AI handling diverse data types, including semi-structured and multimodal data. Large Language Models (LLMs) in AI require preprocessed data for training, while analytics use queries to extract insights from stored data. The article highlights the importance of retrieval-augmented generation (RAG) in AI, which integrates external data for contextual responses, offering a more efficient alternative to building or fine-tuning LLMs. A hybrid data architecture is recommended to accommodate both analytics and AI workloads, addressing challenges such as data access, collaboration, and governance. Starburst’s open data lakehouse solution is presented as a viable option for evolving existing infrastructures to support this hybrid approach, leveraging technologies like Apache Iceberg and Trino for enhanced performance and flexibility.